Method and system for predicting victim users and detecting fake user accounts in online social networks

Abstract

A system and method for predicting victims and detecting fake accounts in OSNs, comprising: a feature-based classifier for predicting victims by classifying, with a classification probability, a target variable of each user in the OSN social graph; a graph-transformer for transforming the social graph into a defense graph by reassigning edge weights to incorporate victim predictions using the classification probability, a graph-based detector for detecting fake users by computing through the power iteration method a probability of a random walk to land on each node in the defense graph after 0(log n) steps, assigning to each node a rank value equal to a node's landing probability normalized by the node degree, sorting the nodes by their rank value and estimating a detection threshold such that each node whose rank value is smaller than the detection threshold is flagged as representing a fake account.

Description

FIELD OF THE INVENTION[0001]The present invention has its application within the telecommunication sector, and especially, relates to Online Social Networking (OSN) services, such as Facebook, Twitter, Digg, LinkedIn, Google+, Tuenti, etc., and their security mechanisms against attacks originating from automated fake user accounts (i.e., Sybil attacks).BACKGROUND OF THE INVENTION[0002]Traditionally, the Sybil attack in computer security represents the situation wherein a reputation system is subverted by forging identities in peer-to-peer networks through creating a large number of pseudonymous identities and then using them to gain a disproportionately large influence. In an electronic network environment, particularly in Online Social Networks (OSNs), Sybil attacks are commonplace due to the open nature of these networks, where an attacker creates multiple fake, each called a Sybil node, and pretends to be multiple real users in the OSN.[0003]Attackers can create fake accounts in ...

AI Technical Summary

Benefits of technology

This patent invention aims to protect social networks from fake accounts that can trick users into connecting with them. It does this by predicting which users are likely to have relationships with fake accounts and then warns them that connecting with these accounts could be risky. This approach helps users make informed decisions and avoid falling prey to these automated attacks. The invention also incorporates a retroactive detection method that can handle a larger number of fake relationships and accounts, ensuring high detection performance with desirable security guarantees. Overall, this invention is efficient and suitable for large social networks with millions of users.

Problems solved by technology

Feature-based detection technique is efficient but does not provide any provable security guarantees.

This way, nodes with rank values close to zero are considered suspicious and represent isolated, fake accounts.

As a result, graph-based detection generally suffers from bad ranking quality, and therefore, low detection performance, rendering it impractical for real-world deployments, including for example multi-community scenarios.

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